DVM-CAR: A Large-Scale Automotive Dataset for Visual Marketing Research and Applications

Huang, J., Chen, B. , Luo, L., Yue, S. and Ounis, I. (2023) DVM-CAR: A Large-Scale Automotive Dataset for Visual Marketing Research and Applications. In: 2022 IEEE International Conference on Big Data (IEEE BigData 2022), Osaka, Japan, 17-20 Dec 2022, pp. 4140-4147. ISBN 9781665480451 (doi: 10.1109/BigData55660.2022.10020634)

Abstract

There is a growing interest in product aesthetics analytics and design. However, the lack of available large-scale data that covers various variables and information is one of the biggest challenges faced by analysts and researchers. In this paper, we present our multidisciplinary initiative of developing a comprehensive automotive dataset from different online sources and formats. Specifically, the created dataset contains 1.4 million images from 899 car models and their corresponding model specifications and sales information over more than ten years in the UK market. Our work makes significant contributions to: (i) research and applications in the automotive industry; (ii) big data creation and sharing; (iii) database design; and (iv) data fusion. Apart from our motivation, technical details and data structure, we further present three simple examples to demonstrate how our data can be used in business research and applications.

• Item Type: Conference Proceedings
• Additional Information: The first author acknowledges the Adam Smith Business School and the School of Computing Science of University of Glasgow’s funding support for this research. The second author would like to thank the funding support of the Region Bourgogne Franche Comte Mobility Grant, Nvidia Accelerated Data Science Grant and Google Cloud Academic Research Grant.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Ounis, Professor Iadh and Chen, Dr Bowei and Huang, JingMin
• Authors: Huang, J., Chen, B., Luo, L., Yue, S., and Ounis, I.
• College/School: College of Science and Engineering; College of Science and Engineering > School of Computing Science; College of Social Sciences > Adam Smith Business School > Management
• ISBN: 9781665480451
• Copyright Holders: Copyright © 2022 IEEE
• First Published: First published in Proceedings of 2022 IEEE International Conference on Big Data (Big Data), 4140-4147
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher

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